Instrumentation & Measurement Magazine 25-9 - 48

Fig. 7 shows the confusion matrix of the classification results
of the decision tree model for No. 10 pepper. The accuracy
of pure pepper powder was 100% and the accuracy of adulterated
wheat bran powder was 86.96%. Compared with the
results of SVM with the same training and test set, it was observed
that the pure pepper can be recognized with the highest
accuracy for all samples; the adulteration of rosin-mixed
power would often be recognized as the adulteration of corn
flour, which may be because similar VOCs could be released
from rosin and corn.
On the other hand, a decision tree model was also built
Fig. 7. Confusion matrix of classification results of No. 10 pepper with
decision tree, where labels 1 to 5 indicate the pure pepper mixture with bran
powder, rosin powder, corn flour and rice bran powder, respectively.
wrongly recognized as the bran mixture. The average recognition
accuracy of decision tree algorithm was 87.40% for
pepper No. 9.
Table 7 lists the accuracy of No. 10 pepper using the decision
tree model. It is noted that the decision tree mode1 also
had good classification results for the categories of pure and
rosin-mixed powder of the No. 10 pepper with accuracy of
91.7% and 94.91%, respectively. But the classification accuracy
for rice bran-mixed powder was relatively poor, at only
70.04%. Moreover, the decision tree algorithm had similar
average prediction accuracy of 87.59% for No. 10 pepper compared
with that for No. 9, which indicates that the decision tree
had similar prediction accuracy to the SVM method to recognize
the adulteration.
based on all samples of ten types of pepper powder and adulteration.
The results of two experiments are listed in Table 8.
In this case, the maximum recognition accuracy with the decision
tree model was 97%, while most of them are below 90%.
Compared to the results with SVM listed in Table 3, the average
accuracy of the decision tree in Table 8 was lower than that
of SVM for most of samples.
Random Forest Algorithm
With the same training and test data sets of the No. 9 and No. 10
pepper samples, the models based on random forest method
were built. The prediction results of the random forest algorithm
for No. 9 pepper powder are shown in Table 9. It is noted
that the recognition accuracy of both pure pepper powder and
rosin-mixed powder reached 100% in three runs, which means
that the random forest algorithm performed excellently for
the classification of these two categories. The classification
accuracy of the random forest algorithm for rice bran-mixed
powder was relatively poor, while the average recognition accuracy
reached higher than 90%. Moreover, and the average
recognition accuracy of each category was higher than 90%,
and the overall average accuracy reached 96%. Therefore, the
Table 8 - Training and testing results of all samples of ten pepper species
Number of
experiments
1
2
Average
Training
Testing
Training
Testing
Training
Testing
1
97.5
89.47
96.75
88.95
97.13
89.35
2
98.75
93.5
99.75
96.50
99.25
95
3
97.25
84
96.75
83
97
83.50
4
96.5
87
97
89
96.75
88
5
98
91
97.5
91
97.75
91
6
98.75
86
98
84
98.38
85
7
99
89
97.75
91
98.38
90
8
99.75
97
99.5
96
99.63
96.5
9
97.5
87
98.5
92
98
89.5
Table 9 - Prediction results for No. 9 pepper powder with random forest algorithm
Number
1
2
3
Average
48
Pure pepper
powder
100%
100%
100%
100%
Bran mixed
powder
100%
94.44%
95.45%
96.63%
Rosin powder
mixed
100%
100%
100%
100%
Corn flour
mixed
88.24%
91.30%
100%
93.18%
IEEE Instrumentation & Measurement Magazine
Mixed with
rice bran
powder
95.24%
92.31%
84%
90.52%
Average
96.70%
95.61%
95.86%
96.06%
December 2022
10
97.75
86
97.25
87
97.50
86.50

Instrumentation & Measurement Magazine 25-9

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